Main Concept
- You need good data for Model Fine-Tuning.
- Data Curation is the process of selecting high-quality relevant data for you specific use case.
Key Aspects
- Data governance helps provide proper data handling, including privacy compliance and ethical considerations.
- Datasets size requirements vary by task, but quality often matters more than quantity
- Careful balance between too little data or under fitting, and too much data, or potential over fitting is essential.
Data Labeling
- Data Labeling must be accurate and consistent
- Data Labeling can be done by experts or crowdsourcing
- Representativeness helps make sure the datasets covers all relevant scenarios and user groups, minimizing bias.
- Special attention mus be paid to edge-cases and diverse examples that reflect real world usage
- Regular data quality assessment helps maintain high standards throughout the Fine-Tuning process